Welcome to Artificially Intelligent Marketing, a weekly podcast where we stay on top of the
latest trends, tips, and tools in the world of marketing AI, helping you get the best
results from your marketing efforts.
Now let's join our hosts, Paul Avery and Martin Broadhurst.
Hello everybody, it's Paul Avery here.
Welcome to episode 39 of Artificially Intelligent Marketing.
I'm here with my co-host and the lovely Martin Broadhurst.
How are we Martin?
I'm splendid.
Just splendid.
That's it.
A single word will do it.
I'm not splendid.
This has got nothing to do with marketing or AI, but we do talk about football or soccer
on the podcast sometimes.
Usually it's me teasing Martin about just how useless Derby County are, but I'm a Liverpool
fan and today our talisman, Jurgen Klopp, has announced he'll be leaving the club.
So I'm really going to have to muster energy today, Martin, for this conversation because
I'm really sad.
Yeah, it was a big announcement.
I was shocked, but I could see why it might impact you.
There is going to be a grieving process to grow through, to go and grow through.
So yeah, I accept there's going to be denial, anger, and at some point you'll land on acceptance.
Hopefully I can get to that quickly.
Fortunately, as much as that's big news, there's been more big news in the world of marketing
and AI.
We've got some interesting stuff to talk through today.
We've got the UK government publishing some more guidance on generative AI from employees.
We're going to dive into it.
I think that's going to be really interesting.
We've got Google releasing a new text-to-video model that none of us can play with yet, but
as usual, the demo videos look awesome.
So this could be the next improvement as we iterate our way up towards production-level
video quality from generative AI.
Runway, one of our favorite companies, as listeners will know, we're going to talk about
what they've been doing to improve their video tool.
We've got some new enhancements and cost reductions coming to OpenAI's platform for those that
use the API.
Eleven Labs, Voice Synthesis fame have got a ton of cash to improve their product even
further.
We're going to talk about the ongoing evolution of AI website builders and is it all hype
or are they actually any good?
We're going to talk about that as well.
We've got an MIT study on whether or not AI is going to replace us all.
That's going to be quite interesting.
Obviously, we've got, for those of you that watch TV, you'll have seen the ads for Samsung
Galaxy S24 with its AI baked in and the rise of AI phones and then a bunch of other stuff
all in between.
I think, Myon, between us, we're going to help me get over the Yergin Klopp news by
looking at all this exciting AI news instead.
What do you reckon?
Well, there's plenty to go at and if nothing else, it'll be a short-term distraction.
Well, let's do it.
Let's get stuck in.
Let's talk about this new guidance from the UK government, Myon.
They've published a generative AI framework for HM Government, which is a 70-odd page
document covering all things about safe, responsible, and effective use of generative AI in government
and the public sector.
I've been fairly critical of this government and certain episodes in the past, I've given
them a bit of a bashing, but this document is very good.
I applaud them.
I think they've done a really comprehensive job when it comes to explaining what AI is
and generative AI, explaining how to use it, giving some examples, talking about how to
procure it, talking about the ethics and responsible use.
It's a very broad document, but the language that they write in is plain English.
It's technical without being overly technical.
If you're just a departmental head in a local authority, you could pick up this and read
it without needing to know anything about deep learning and neural networks and all
of that kind of thing.
They've done a really good job.
It's based around 10 core principles and they're very easy to understand.
There's no complexity to it.
Now, the 10 core principles, I'll give you a summary of what they include.
It talks about you should be operating with an understanding of what generative AI is
and its limitations, recognizing what it can and can't do.
You should understand what it is to use generative AI in a lawful, ethical, and responsible way.
They talk about security, data privacy, implementing technical controls about who can access the
data.
They talk about meaningful human control, which is about emphasizing the necessity of
human oversight.
They're kind of human in the loop, the kind of thing that we talk about all the time on
the podcast when it comes to using these systems.
There's more as well.
I'm not going to go into all of them, but they very much talk about skills and expertise,
being collaborative with commercial colleagues from the start.
I think the thing that really stood out to me is that they're really taking generative
AI and the potential benefits as well as the potential downsides and risks.
They're taking it very seriously.
They're not shying away from this.
This is very much an innovation first approach from government.
I'm very keen to see it.
The thing that really came through for me in the forward and in the executive summary
is they're really pushing AI literacy.
This is big to the point of, well, you can see this as a big change management piece
for them as evidenced by additional resources outside of this PDF being made available.
One such document is a poster that lists the 10 principles and the 10 core principles of
using generative AI.
They're encouraging departments and managers to access these posters and put them up around
their workplace.
You're going to start seeing local councils and all these government bodies having posters
talking about best practice for using generative AI.
They are not shying away from it.
They're really, really leaning into it.
Is this document, this is for internal use within county councils and government institutions?
This is not more broad advice for small businesses, for example?
No, this is very much for internal use within government, local government, NHS, all these
government departments.
It's about how to use this in a safe and responsible way.
However, it applies to everyone.
You could pick up this document and do a find and replace government for my business and
it would be just as valid.
It's a really well crafted document and I think it's plain English to the point where
anyone can pick it up and learn from it.
I think that's important because the world of generative AI, the technical aspects become
quite opaque.
I think even one of the challenges we sometimes face on the podcast is trying to walk that
line between transformer architectures and retrieval augmented generation and all of
this sort of buzzword bingo that most people are probably like, I don't know what that
is so you've just alienated me and I don't really care about how it all works.
I want to know what I can do with it.
It sounds like they've bridged that gap to try and make sure that people who are not
inside the system can still benefit.
But I guess the other question I would have is how instructive is the document?
Is it like, hey, you should use AI lawfully and ethically and then everyone's like, yeah,
that makes sense.
But then does it have instruction on how best to do that or does it not really go into that
detail?
There's lots of directional and kind of signposting to other documents and talking about like,
here's a document that we've published about cyber security and here's a document about
this that we've published.
It is a bit instructional as well.
Each of the core principles has a page or two going into more depth about it.
So yeah, like I say, it's a good practical document.
It's not short.
It's a 70 odd page resource.
This isn't like a five page pamphlet that you'd sit and flick through in your doctor's
waiting room kind of thing.
There is a little bit of detail in there that is very helpful.
So would you advise then for SMEs, for example, thinking about, oh, crumbs, at some point
I'm going to have to have a look at this generative AI thing and start to think about how we use
it in our business.
Could this be like a good first step for them to read this document?
Very much so.
And not just SMEs, you know, any organization, if you're a senior executive in enterprise,
this document is as instructive for you in your role as it is for an SME entrepreneur,
owner, managing a team of 20 to 200, whatever it may be.
20,000 people.
Think about how many people are in the NHS, right?
This is just some really solid principles.
The 10 core principles that they've come up with are very good.
There's also some other bits in there that are really, what's the best word to describe
it?
Grounded in the present reality.
So there's a section about procurement and how to procure generative AI.
And there's a whole page on it, which is basically procuring technologies in an emerging market.
And it talks about the risks and the limitations and how do you assess vendors when new ones
are popping up all of the time.
Now, this is only a page or two.
It's not a whole textbook on it, but they're clearly recognizing that, look, this is fast
paced.
This is changing.
That if you buy something today, the situation could be completely different in six months
time.
So you've got to make sure that you're identifying the right use cases.
You're evaluating the technology using some really solid principles.
Yeah.
Overall, a great document.
Yeah, I love that.
I think that's some of the critical things.
We talk about some of those things on the podcast quite often, but I think they are
critical when it comes to getting started, figuring out the right tools.
As we talked about a couple of times, there's so many strands to this.
Technology is a key driver and the emerging tools and the emerging capabilities, but the
human part of this, the change management part is just as critical.
So yeah, nice to see a document that is a good sign poster for internal use within the
UK government and associated infrastructure.
But also it sounds like Martin, a document that others can use as well to try and get
some insights into how they can move ahead with generative AI.
Yeah.
I'll make sure I pop a link in the show notes if anyone wants to access the document themselves.
Well, I think that would be extremely kind of you and a good idea.
Cool story.
Thanks for finding that one, Martin.
Let's move on to our next one.
So Publicis Group, which is a pretty large global advertising communications company
that some of our listeners might have heard of, have announced over the last week or two
an ambitious investment of over 300 million euros in AI over the next three years, alongside
the introduction of its new AI platform, which it calls Core AI.
The company aims to enhance its capabilities across all of its organization with investment
in both technology on the AI side, but also personnel development.
Core AI is the centerpiece of Publicis Group's AI strategy, and it's designed as like an
intelligent system that's going to incorporate things like foundational AI models, AI agents,
there's going to be an API layer for moving information around, and also partnerships
with third party AI services like OpenAI, Adobe, Amazon, Microsoft, people we often
talk about here on the podcast, to basically connect trillions of data points and a database
of 2.3 billion consumer profiles to influence how the agency groups do business, right?
In terms of insights and strategy, media planning, creative and production, software development,
operations, and all that good stuff.
The market liked this because Publicis Group's stock price reached a record high off the
back of this news.
I think for people like you and I, Martin, you and I, you're an ex-agency and I currently
lead an agency, it's interesting to see these big agency groups, some of them, in this case
Publicis, going all in on AI.
I can't say I'm surprised.
I think it probably does take organizations or groups with this heft, right?
This size of company to try and bake AI in in this way.
But yeah, it's an interesting one.
What were your thoughts on this news item?
Not surprising when you see that they've got huge amounts of data that they're sat on.
Digital has been at the core of the business model of these huge agencies.
They've typically been going around acquiring businesses in the data insight and analytics
world for a while.
So they've got lots of top talent, typically speaking.
I'm not sure specifically about the acquisitions of Publicis, but you see them in headlines
about acquiring those kind of organizations regularly.
I think this makes sense if you're going to get a competitive edge, then you've got to
make the data that you've got available to you work for you and how else can you do that
other than building your own AI layer.
Makes sense that they've productized it and given it a brand name, Core AI, gives them
something to take to the market and hang their hat on when they're going to put pictures
and to be able to say, we've got our Core AI infrastructure.
I'd love to see what that actually looks like.
How is that actually manifesting itself within the team?
Is it a front end system that people can plug into or is that more of a process and a way
of operating?
What does it mean?
They talked about the API layer, so there's got to be some actual technology underpinning
it all.
It's not just plugging a bunch of tools into one another.
As with all of these things, without getting your hands on it, you can't say much more
than, oh, it's an interesting sounding press release that makes their stock price go up.
I would welcome the opportunity to speak to some of the AI team at Publicis to talk about
what their vision is for it and where it goes from here.
Yeah, I think you're right.
A lot of this, I think, is that branded AI inside play to help them differentiate themselves
against the other big agency groups until the other big agency groups make these types
of announcements, which are probably not far off.
I think you're right in terms of how data centric a lot of these groups have become
over the years.
It's almost like their mini but slightly diversified Facebook approach.
Facebook's got loads of data on us.
They're using AI to help surface content and show ads to the right people and even
to generate ads now.
You could definitely imagine how Publicis Group could use similar tools internally for
audience creation and segmentation for their clients, predictions in terms of consumer
purchase habits and then building strategies, content creation, programmatic advertising
off the back of that.
I don't even know how much their clients are going to get to see of how that machinery
works if I'm honest.
I think it'll be more, these are the outcomes that we can get.
These are the outputs that we'll produce.
There's AI magic in here and AI is buzz and cool and we're buzz and cool and you should
work with us.
I suspect this is where a lot of that will come down to.
There are some tools.
It's nice that they're creating their own infrastructure and tools to use but there
are some tools that hopefully lots of people are going to be able to use like Google's
new text to video model, Martin.
Yeah, nice to see Google Research publishing new papers.
They've just developed a new model called Lumiere which is a space-time text to video
diffusion model capable of transforming text and images into realistic AI generated videos.
Now I am not going to pretend that I know what space-time text to video diffusion model
actually means but what I can tell you is that what makes this model slightly different
is it stands out for its ability to generate full frame rate, low resolution videos in
one pass using what they're calling the space-time unit architecture which processes multiple
space-time scales.
I read this story and felt overwhelmed with technical input.
I was like, I need someone to explain this like I'm five.
So I asked Perplexity to explain it like I'm five and it did a good job but it basically
did just say this is a computer system that can make videos and it looks at an image and
it makes an image.
There you go.
Right, right.
Okay.
Maybe you've dumbed it down slightly too hard for me there.
But yeah, this is an interesting development.
Having seen the demo video, again demo videos, right, always got to be wary particularly
with Google's recent track record of demo videos and people being scandalized but the
demo video looks cool.
The videos look high quality.
They've trained it on a data set of 30 million videos and text captions.
They've not disclosed any of the training data set but let's be honest, they've got
enough data that they're sat on.
It can do text to video generation, converts still images into videos, generates videos
in specific styles using a reference image and it does video inpainting.
So its capabilities are quite broad, certainly much broader than we see from some of the
other text to image generators on the market.
It produces five second videos at the moment and it's not available for us to use.
This is very much just a research paper but I expect that we'll see some things on the
market in the near future based on how long did it take for the imaging to come to market.
It was about the best part of a year wasn't it actually?
Yeah, I think my gut on this stuff is that sometimes we see the products accessible in
some sort of demo form in about three months but in terms of it being baked into something
that you could use on a sort of more regular basis, yeah six months maybe 12.
So hopefully it will come soon.
I mean watching the demo videos and trying to understand this space-time which just sounds
really cool anyway.
Like let's give them props for that.
I feel like I'm in Star Wars or Star Trek or something.
Is this ability to create more realistic motion?
Because one of the issues when you're trying to create motion in tools like Runway is people's
heads morph weirdly, even items in the videos morph weirdly.
So it's kind of hard to get a moving image to video generation where the original image
doesn't get mangled in some way during the process so that you look at it and you're
like that's clearly AI generated.
Even the best examples for me are super obviously AI generated.
I think what reading between the lines here the nature of how this model works should
ensure that frame to frame the image is animating or sort of moving in a way that's more realistic.
So if someone turns their head it should look like they're normally turning their head.
If their hair blows in the breeze it should look more natural.
If a bow is moving left to right it should look more natural and I think that's what
they're claiming.
So I think you said until we can actually get our hands on it and play with it because
the gap there will be a gap between the demos and the real world use because as we talk
about a lot and this might be user error more than anything but you and I struggle to get
anything that looks particularly good out of Runway if we're honest.
I think you have to spend ages refining your prompt and thinking carefully about your input
image to get stuff that looks really good.
I would love this to be a step forward in usability for your average user so you can
just chuck an image in put in a prompt to get a half decent result without all of the
garbledness that seems to plague most video generation tools at the moment.
That is the dream.
While we're on video generation tools and we've mentioned Runway who we think are awesome
by the way and doing great work in this area they are not resting on their laurels either
because they have upgraded their AI video generation capabilities with the new multi-motion
brush in their flagship Gen 2 video generation model.
So many of you might know from previous podcast episodes when we discussed the introduction
of the original motion brush which was interesting because rather than just uploading an image
as a prompt and then Gen 2 tries to animate the whole image you could use the motion brush
to say to just paint a certain bit of the image and just encourage the model to only
animate that.
So an example would be a boat on a river but you don't want but there's loads of other
things that are in the shot but you don't want those to animate so you just paint the
boat and then ideally the boat sails along on the river.
Well now with the multi-brush multi-motion brush feature instead of just painting one
thing you can now paint multiple things that will animate in different ways.
So you might want one boat going one way but you might want another boat going in the opposite
direction which in theory now you can do because you can paint the image in essence with different
brushes in different colours and have them do different things.
So this is kind of cool because it's part of the ongoing evolution of how these video
tools are maturing giving users ever greater control about which parts of an image assuming
they're using an image in their prompt that they want it to animate.
I haven't actually had a play with the multi-motion brush multi-brush motion crumbs it's a bit
of a mouthful.
I don't know if you've had a play with this yet.
Mark I've seen some of the videos that look pretty cool but I haven't actually tried it
out and broken it like I probably will.
Not with the multi-motion I tried the motion brush and just failed.
I think with these video generation models there's a...
Okay as with any AI you've got to understand the limitations and where it's currently at
and I think I still not found where that kind of sweet spot is when I'm using these tools.
I understand the limits of what GPT-3 can do and I understand how to get good value
and good outputs from GPT-4.
Darley and stable diffusion and mid-journey I'm getting good results from those and I
know where I can push the boundaries and where to row back.
But video I can't get there and I try and I try different prompting techniques and it
does take time.
So I think there's definitely an element of user error about this but I also think the
models, the video models just still are a little bit...
And that's probably why we're not seeing lots and lots of examples coming out of people
creating great stuff.
And actually when I see a video come out and bearing in mind we're both in these communities
online when you do see some of these videos come out they're kind of noticeable because
they're an exception.
I think a lot of people are having similar struggles in finding where these models work
well and where they fall apart because I'm really good at finding where they fall apart.
Yeah I think you've nailed it there because the best examples I see are people creating
sort of trailers or like very short movies that are like 60 seconds long that are fast
cutting because these tools can only really produce two, three, five seconds of video
that has like it starts to lose its coherence the longer it goes.
And so they're cutting quite quickly.
They're trying to maintain consistent characters in between different shots if you like.
And I suspect that in most cases they're using image prompting.
So the image is in the prompt to help really direct the system on how which, what motion
to create what the video output video should look like.
For me this is still in the hobbyist have some fun with it phase.
I don't think it's got the necessary production level power to be of real use to marketers.
I'm going to stick my neck out and say that.
My use case that I expect to come first but again I'm frustrated because I can't get
a decent output reasonably quickly is just adding some motion to things like social media
static images that would have been static but it would be really great to just animate
a few bits of them.
To be honest the go-to for me there is still probably the little animations that Canva
can do which are very templated.
They look like sort of standard PowerPoint slide type animations.
They're not particularly interesting.
I think when you can take a branded image introduce a bit of motion in runway and then
run that on social there'll be a time period where that's interesting and you'll get attention
online because you're doing it and then it will become passe and everybody's images will
do it and then no one will pay attention anymore.
So that's the sort of that's I think going to be the first use case that as a marketer
unlocks it for me but as I say it's far too much effort to get anything half decent for
me at least out of it for even that simple use case.
So until we cross that barrier I'm playing with it.
I'm not thinking about how I can use it for work if I'm honest.
Yeah exactly the same.
Right let's move on from video and let's talk a little bit about OpenAI's announcements
this week.
Martin tell us a bit more about these enhancements and cost reductions that were shared on the
Twittersphere.
Small changes but significant ones nonetheless.
They've announced some updates on their text embeddings models as well as some improvements
and optimizations to GPT-4 Turbo and GPT-3.5 Turbo.
Since GPT-4 Turbo was launched one of the criticisms especially from developers was
that it had become kind of lazy.
If you asked it to write some code it would not give you the full coding.
Truncate the code.
It would tell you to add in bits here and there and put lots of comments in it saying
and this is where you need to integrate this with that.
And sometimes you just wanted it to give me the full code.
I've given you 40 lines of code.
I wanted you to fix the bug and give me the full 40 lines of code back fixed.
But instead what it does is it gives you like 10 lines and says copy and paste this here.
Well it sounds like they've fixed that.
They've made it less lazy and giving it a more complete output.
They've also improved some issues that were reported with non-English UTF-8 generation.
So this is some of the characters that it would generate.
It wasn't very good and it would be a bit buggy.
So GPT-3.5 Turbo they've made some improvements here on the cost front.
So now the model offers 50% lower input prices and 25% reduced output prices making the model
more affordable.
And I think they've made some improvements to the model in terms of accuracy and format
response capabilities.
But the big headline here is it's substantially cheaper to use than it was previously.
And that matters.
I've been doing a project recently where we've been working with a client to help improve
their analysis and insight extraction from lots of qualitative data.
Spreadsheets with 70 rows of text inputs.
Probably 30 columns of data formatted badly, completely inconsistent in terms of the responses
you get from people.
And using the GPT for Sheets plugin, which is excellent by the way, if anybody wants
to integrate the OpenAI models with Google Sheets, I highly recommend that as a plugin.
But also integrating the Claude for Sheets plugin.
I've been playing around trying to get insight extraction and it gets expensive very quickly.
Especially when every time you load the sheet, so every time you open it, unless you can
turn on caching, so it stores the data for six hours, but if you don't have that turned
on, it refreshes every cell and runs the prompt again.
And I found that when I had GPT-4, not GPT-4 Turbo turned on, and GPT-4 Turbo is considerably
cheaper than GPT-4, just refreshing the spreadsheet once ended up costing me a couple of quid.
So just to run it once.
So that is quite expensive and not something that you want to do regularly.
But yeah, GPT-3, GPT-4, if we can get the prices down, that's going to be better for
everyone.
Just on the insights extraction piece actually, what was fascinating to me was having columns
with the analysis of each row from GPT-3.5, GPT-4 and Claude 2.1.
The difference in quality and detail with the exact verbatim same prompts was really,
really insightful.
GPT-4, and this should come as no surprise, was far and away the more detailed, the more
insightful, the more nuanced.
Claude 2.1, I would say was very, very, very similar to GPT-3.5.
If anything, maybe marginally fractionally worse, but that could be down to the style
of the prompt.
I mean, they were basically on a par, but the difference between 3.5 and 4 was night
and day.
The frustration from a practical perspective, if anybody is going to implement GPT for Sheets,
is be aware that Google Sheets has a 30 second timeout if you're making an external API call.
So without getting too technical on it, when you run that query and it sends that query
and you're prompt off to open AI, if it doesn't get a response back within 30 seconds, you
see an error message.
And the problem with GPT-4 and GPT-4 Turbo is the latency on these models is quite slow.
So if you've got quite a long prompt and you're expecting quite a long response, it can be
more than 30 seconds, therefore you'll get an error message.
And this can, let me tell you, be infuriating.
Yeah, for those...
We've talked about this quite a few months ago, so if you're new to the podcast, you
might not have heard this story, but in the original days of the podcast, one of the key
drivers for Martin and I was we want to use AI in as many areas of podcast production
as we can, because that's kind of half the point of doing the podcast, is to see what
can AI help with, right?
Having two humans having a conversation should be one of the most human driven things that
can exist, but we found ways to infuse AI in a number of areas.
And one of the things that we tried was using Google Sheets, using a plugin like this and
having GPT-4 write the script for the podcast based on the stories that we copy paste into
it.
So in cell 1A, you copy paste your story and then this is a simplification, but in cell
1B, it sends that story to GPT-4 with a set of instructions and GPT-4 writes a script
off the back of it and then it populates that cell, which was great, except three quarters
of the time it would time out, which basically made it unusable, right?
Because it was supposed to be something that would save us time, but you spent so many
times rerunning, rerunning, rerunning the prompt until you actually got an output, whilst
burning API costs the whole time.
So that 30 second window is a pain and it causes a lot of issues, but if you're listening
to this, I think Martin's use case there is really awesome and I've had a number of people
when I've talked about this out and about really thinking of interesting ways to use
spreadsheet data to automatically drive GPT-4 outputs.
So another great example would be you want to do some sort of cold email outbound prospecting
process, but you want your emails to be somewhat customized, while now if you have column A
has maybe the person's name, column B has some information about their company and column
C has the particular product that you think they'll be interested in, you could now create
customized prospecting emails that are more than just the smart content that we use in
a lot of the automation systems today.
You know, hi first name, how's it going over at company, right?
They're going to be better than that.
So I think we're back to these are the tools, this is the practical thing they can do, right?
They can run prompts and generate information from GPT-4 at scale.
How are you going to structure a spreadsheet to drive value in your marketing and sales
organization using it, whilst taking on board Martin's really helpful comments about the
levels in quality difference between GPT-4, 3.5 and Claude, the cost differences, you
know, you've got to get that cost benefit ratio right and that darn 30 second timeout
that's going to trip you up if you try and do something too lengthy or complicated that
GPT-4 just struggles to produce the information in the right time.
Cool, thanks for sharing that with us Martin.
Can I ask you a quick question actually?
Who's using GPT-3.5?
So reducing the cost for it, so they obviously think some people are going to benefit from
that.
Like what's the use case for that?
Because we've just talked about how much better GPT-4 is.
So who's using GPT-3.5 in these things?
Yeah, so GPT-3.5 I think still does an excellent job, particularly with a well crafted prompt.
In fact, in the project I was just describing, the next iteration for me is to rather than
having zero shot prompting for GPT-3, what I'm going to do is take the output that I
got from GPT-4 and give it that in the prompt for GPT-3.5 to kind of give it an example
of the level of detail and information that I want.
So I'm going to give it a few shot prompt.
And I think that's where GPT-3.5 will come into its own because it's really good with
few shot prompting, but it means your input costs are higher because you've got to do
much longer.
You've got to do much longer prompting.
But when it sees that pattern recognition, it does a very good job.
Also for a lot of applications, GPT-3.5 again with few shot prompting is really good, you
know, writing social media posts can be very good if you've given it some examples.
Its classification is really good for that.
Doing sentiment analysis, really good for that.
So I think there is a lot of use cases, but I think for most people as an assistant, you
know, that kind of back and forth dialogue, help me solve this problem.
GPT-4 is just much better.
But where you have a particularly a use case like we've got the podcast, we could, we don't,
but we could get the RSS feed from the podcast, put it into spreadsheet, taking episode title,
show notes, publish date, etc. and just have a series of columns giving us tweets, giving
us LinkedIn posts, giving us email subject lines in our style and our tone of voice because
we've done that in the few shot prompt where we give the prompt examples of ones that we
like and then it gives us the output.
So I think that's where it can be applied quite effectively.
That's quite cool because then we can use a tool like Zapier to just automatically schedule
those posts.
Ideally with a human in the loop, so whatever tool we're using, we can go and check and
make sure they're not absolute nonsense and then they're ready for posting.
Because nobody would ever do that, would they Paul?
I definitely haven't done that in the past on this very podcast and shared posts on LinkedIn,
fully automated and gone, ah, I didn't mean to do that.
Yeah, when I had to forward that one to you and I was like, it's Friday, I'm quite tired
Martin, but this makes no sense, does it?
No, sorry, that was an AI generated one that slipped through.
It happens to the best of us.
Right, let's move on to our next story then Martin.
We're going to talk 11 Labs briefly because they just got a ton of funding.
So for those of you that are new to the podcast, 11 Labs is one of the leaders in voice technology,
so they're the ones that have been creating synthetic voices where you write a script
and then it reads it out for you and sounds very, very human.
They've been piloting and driving voice cloning in terms of cloning your own voice and writing
a script that then speaks in your own voice and also trying to introduce other clever
aspects to voice synthesis around maintaining emotional style, translating between languages.
So if you speak in English, it will then translate it and have you speak it in Spanish in your
voice and all that good stuff.
And this week they got $80 million.
That felt a bit Dr. Evil there.
$80 million in Series B funding led by notable investors like Andreessen Horowitz, Sequoia
Capital and all these firms that are very much Silicon Valley tech investment.
But I think this just goes to show how people see this as being big business.
I did see some sort of, will I call it banter?
Yeah, let's go with banter.
But basically people questioning the value, because I think it puts their valuation at
like half a billion or something, and people questioning how defensible their moat is when
Meta is releasing open source models supposedly.
We've seen some demos of what their models can do.
No one can access them yet as far as I know.
They've been very open sourcey so far, have Meta.
So is there a open source voice synthesis model coming from Meta that's basically as
good as Eleven Labs is?
We've got PlayHT, we've got voice synthesis starting to be built into tools in a sort
of rudimentary way like Descript.
So I don't know.
I think that's a fair criticism in terms of just how defensible Eleven Labs' position
is.
We love them, we should say as well.
We think their technology is cool.
We play with it all the time.
We're always looking for cool use cases.
But a lot of money going into a lot of AI apps that might not have a defensible market
position, which is interesting.
When you consider that you can do AI voice generation through Google's cloud AI suite,
Amazon has got a system called Poly, which is just AI voice generation, PlayHT you've
mentioned.
You've got Lovo with their Geni application.
You've got Eleven Labs.
The competition is there.
Plus, as you say, Meta are working on this as well and they're very open-sourcy in the
way that they do it.
So this is going to be a very competitive landscape and it is going to be baked into
tools left, right and center.
Half a billion does seem high.
There's a lot of hype at the moment.
Can they keep the momentum going with new product releases?
They've just launched the dubbing studio, which is a voice library as well.
They've got quite a big voice library.
Just on that dubbing studio that offers enhanced content production capabilities where you
can dub entire videos.
So you can just drop in a whole clip and it will overdub a whole piece.
They've got a mobile reader app, which facilitates the instant conversion of text and URLs into
audio.
They've got a very good API, which even I've managed to build things that integrate with
it.
I'm not a coder, as I've said many times in the past.
So if they can keep innovating and pushing the boundaries and I think what sets them
apart at the moment, and I've actually written a blog post about this, is the quality of
their voices is just better than all of the others.
The nearest I can find in terms of emotional range and having some depth to the voice is
Lovvo and the quality of the outputs you get from Jenny.
But 11 Labs, I think is just the best in terms of quality of voice at the moment.
Yeah.
I mean, and they're buzzing because I think they've only got, you know, they're less than
a hundred employees and this big evaluation.
So I do think they're doing exciting things.
I should say I was summarizing some back and forth on Twitter and LinkedIn questioning
this.
I'm not saying that's like my own personal opinion, although I think it's an interesting
potential marker of the amount of hype inflation that's happening in the market.
But I think their tools are really cool.
I completely agree with that.
I do want to play with Metas though.
The most recent demo I saw, I think it was last week, had people speaking one language,
but with very deliberate sort of emotional expression.
So like being scared, being excited, whispering, and then translating that into another language
spoken in that same person's voice, but maintaining the emotional performance.
You just think about the use cases of this for call centers and all those other things
that we've talked about.
But as marketers being able to create audio versions of all your blog posts that don't
sound sort of crappy and robotic like all voice synthesis has been, will there be an
audio book production market like we know it?
Or will you buy the text version on Kindle and you'll get three voices for free?
But if you want the really cool voices or you know, what happens when famous orators
who have lovely voices, you can now get all your books read for you by Morgan Freeman
because they've licensed his voice out and you can get all your audio books read in his
style.
There's just so many things here that again, I think we're on a cusp of a revolution in
terms of how we use spoken audio in so many different areas.
And I suspect that the likes of Andres and Horowitz are looking at Eleven Labs as being
major fuel for that, where they would think the commercial applications of this technology
are so wide ranging that the revenue is massive that even if there's eight companies that
all go to market with very popular products, they all still make an absolute ton of cash,
which of course could be very possible as well.
Let's talk website builders.
You've been playing, Martin.
Talk to us about how you feel about website builders.
There's been a lot of hype around AI tools and the demo videos, as we've seen and have
in fact talked about today, showing off products in their very best light.
And one that always stuck in my mind was the Wix AI website builder, which looked so cool.
The idea that you could enter your prompt and it would go away and build you this site
and then you could edit the site by further prompting, saying make it more modern, make
it more dynamic, make it more classic or what have you.
And it would make all of these things on the fly.
And the video, I remember at the time we talked about it, just looked so cool and I was really
excited to get my hands on it.
Well that now exists as a product.
You can use it, the Wix AI website builder.
And to say that it is not what I expected based on the video is something of an understatement.
So the mechanism for this is you go on to the website builder and it starts off with
a chat interface and it will say, tell us the name of your business.
You type in the name of your business.
And then it says, tell us about your business and the services and products that you sell.
And then you type in that as well.
And then there's a couple more questions.
Effectively, what they've done is they've turned a form into a chat interface to make
it AI because that's what people do, isn't it?
They chat with chat GPT.
So that's AI, isn't it?
So they give you a form, you fill out the form.
Then on the next screen, you choose the color and style that you want.
But it's a limited range.
So there'll be six options.
And you might be like, oh, I want the green and white one, sans serif font.
So you choose that one.
And then it picks a template and uses the GPT-3 API.
I'm assuming that's what it's using to populate this template with some text.
The thing is, on the examples that I've seen, it's really lacking in depth.
It seems to take none of the input text that you give it from the prompt and turn it into
something relevant on the page.
I think it's trash, to be perfectly honest.
I think it's a really poor implementation of it.
And the reason I wanted to talk about it is because actually, I tried a version of this
with another CMS from a company called Doric.
So Doric is based out of Bangladesh, it's a no-code CMS.
In fact, the artificially intelligent marketing website is built and hosted on the Doric website
builder.
And they literally this week launched their AI website builder in beta.
So it's private beta.
And it's pretty good.
First of all, it's much more like standard prompting.
It says, what's the name of your business and then give us a description of your business
in 1000 characters.
And you give it that and then you hit generate.
And then it chooses one of the templates for you, but it uses your description to choose
the style for you.
So it will, and I've played with this in a few different versions now, and it does a
pretty good job.
Now you can go in and edit the global styles if you don't like what it generates.
But if you're using an AI generator for your website, chances are you're not somebody with
a huge design team.
You're probably a solo entrepreneur.
Maybe you're a marketing consultant or a yoga teacher or whatever.
You're not there building websites all the time with this detail.
So it does a very good job of choosing the style.
And then it literally will build the website.
You can see it filling in all the text in all of the different spots as it goes.
And it does a really good job.
It makes some of the detail.
You can tell in the back end, they've really thought about taking your input and applying
that to things like the product and services, applying that to what this section should
be versus this section.
And you don't see that in the Wix version.
The Wix version slaps in some sections that feel like placeholders.
And it almost feels like the text in there is placeholder as opposed to the direct version
where the text that it puts in feels informed by your prompt.
It feels relevant.
So I think for a really small startup based out in Bangladesh, they have done a fantastic
first stab at this.
And it gives me hope that a decent AI website builder will come to the fore.
And I've also seen the HubSpot AI website builder.
I saw it at inbound and I saw a live demo and that's more similar to the Wix version
in terms of the implementation of it.
It's more of a guided wizard, a WYSIWYG kind of, and what you end up with is a WYSIWYG
drag and drop template, but to get there, you go through a wizard asking you lots of
questions, asking you to fill in some data, pick the style and template that you like
the look of.
And then it creates it with images that it's found from stock libraries and text that it's
generated with with ChatGPT 3.5.
And that does a good job.
That's a good starting point.
But the most pure version of an AI builder that I've seen is the version from Doric.
And I think they've done a very good job for saying it literally launched in beta just
this week.
It sounds like it's worth going and having a play with Doric if you are interested in
these types of AI driven website builders.
I think listening to you talk about that, it really makes me reflect on my biggest challenge
at the moment is I think I've got AI hype cycle whiplash.
I'm like, yes, this is amazing.
Oh, this sucks.
Yes, this is amazing.
Oh, this sucks.
Like I'm in the excited phase.
I'm in the trough of disillusionment and I'm flipping back and forward in a single day,
depending on if I'm looking at video or I'm using GPT-4 for an interesting use case or
I'm looking at a website builder.
I can flip between those states in a single domain like video or if I'm moving between
domains.
That is the biggest challenge at the moment in AI and using it for all these different
use cases because we're seeing an explosion, right?
Text generation, image generation, video generation, audio generation and then layered on top of
that what I would call quasi-thinking agents that can do some brainstorming and thinking
for you, right?
Like turning a prompt into a web page template.
That's a multi-step process that's beyond just getting an output.
We're racing ahead in so many different areas all at the same time.
None of them are in a straight line.
I think as a general user, it's very hard to know where you can obtain value with these
different systems.
I think if you go into using them and then you get a bad first output or it doesn't
really generate the thing that you want, you could see why people quite quickly go, AI
is fun and it's nice but it's not there yet because it doesn't give me the things I want.
I suspect that a lot of people are actually running into that and then thinking and putting
AI to one side for a bit and going, yeah, I can't do the things I need it to do.
Listening to you talk about website builders nicely encapsulates that whiplash for me and
it's definitely something I'm feeling.
If I was going to give any tip to a marketer that is looking to maybe deploy AI in their
product and announce it, maybe trail it with sneak preview type of thing, one way that
you can help people like me and Paul avoid such a whiplash is to just manage expectations.
Don't put out videos showcasing stuff that it can't actually do.
That's not what we need because we're just going to be disappointed when we try and use
it and that's not what you want in your users.
Right.
We're drifting in on time, Martin.
We probably need to swing through these last few stories quite quickly.
Let's briefly touch on this MIT study.
There was a recent study by MIT's Computer Science and Artificial Intelligence Laboratory
with MIT Sloan.
Basically this study looked into the potential impact of AI on jobs with a focus on computer
vision.
The core finding from the study is that only about 23% of tasks that require vision are
currently economically viable to automate using AI.
This is probably one of those research reports that is a counterbalance to other search reports
that are saying AI is coming for everybody's jobs and in one year nobody will have a job
because AI will do everything.
What this study is saying is AI's potential is large, especially on giving computers vision
to see things and do things, but the sheer cost of doing that massively outweighs having
humans do it.
I think this study was quite a nice counterbalance to some of those other studies and it further
shines the light on the cost of using AI.
There was another story this week about just how much energy consumption is used to run
these AI models.
I'm going to get this quote wrong, but there was a quote, I think it was in the Wall Street
Journal that was something of the equivalent of asking a simple question of chat GPT is
like driving a Lamborghini to do pizza deliveries.
I think that's probably true as well.
You see Sam Altman talk a lot about the next revolution we're going to require is going
to be energy generation because the energy that we're going to need to run all of these
server farms and all of these graphics processing units or these emerging chip architectures
to drive large language models, it's all going to take a lot of energy, a lot of development
of other technologies.
So, as folks, as humans, we've got a little bit more time maybe before AI comes and takes
on our jobs than we thought.
Yeah, that's an interesting point from Sam Altman, particularly as he has his fingers
in the energy pie, doesn't he, with Helion, the nuclear energy startup.
That's no doubt going to be powering those server farms and the rest of us for years
to come.
Yeah, if you look inside baseball on this one, the fact that Sam Altman, you see these
different things emerge where he's involved in companies designing new chips, right, because
he's clearly going to see how many chips the world's going to need if everything's powered
by AI.
He's looking at energy companies because he's like, I know how much energy is going to be
required to run those chips.
If you're inside the system on this, you can probably make some pretty healthy bets on
where the economic drivers are going to be that enable this AI revolution.
So it doesn't surprise me.
In fact, it's probably worth watching what a lot of these people are investing their
time and their money in outside of AI because they probably know things that we all don't.
Not that I'm qualified in any way to give investment advice, and I'm not, so please
don't take my advice, but I just think it's interesting.
Let's talk mobile phones, Martin, because we've been talking about one of the cool
things that we probably will see over time is that small models can be run natively on
a phone without having to go up to the internet and servers, which obviously has a range of
benefits that we've talked about.
But Samsung Galaxy S24 has been announced and released over the last week or so, and
they're, to my knowledge, the first big phone manufacturer to lean into AI in a big way.
So what do we have that the S24 is going to be bringing to the market?
It's really an AI forward mobile device.
They've really leaned into the messaging and basically the chipsets and the technology
independent allows there to be a lot of AI on device.
So they talk about live translate and an interpreter offering real-time voice and text translations,
which on the demo looked very cool.
They've got the Pro Visual Engine.
So this is a comprehensive suite of AI powered tools for image capturing and editing.
So things like enhanced night photography, high quality video editing and photography
manipulation on device.
We've got this intuitive search with circle to search.
So this is a new gesture driven feature, which allows Google to search by simply drawing
a circle and highlighting something on the screen.
So it will do a kind of Google image search or something similar with that, which is a
great new input.
I always like these new interfaces where you get a new way of interacting with tech.
I'm like, that's pretty cool.
That one is cool.
Because if you think about, for marketers, we talk about how if we speak to computers
most of the time, instead of typing and reading stuff, I'm not necessarily saying it's all
or nothing on that, but as user behavior changes there, it just puts more pressure on search
engine optimization as a strategy.
So many things we've talked about on the podcast in terms of Google's generative search experience
and it's showing more and more answers in the search pane and not driving more traffic
to your website.
Then we've got people using what we expect is imminent in terms of smarter assistance
that we can ask questions of that.
Again, we're not looking at a search results page.
We're not clicking through to other websites.
Now we've got circle things in images that you think are cool and then Google will search
for content around that.
The potential complexity and challenges ahead for SEO professionals over the next two or
three years, I don't think should be underestimated as people's engagement mechanisms with computers
and phones evolves to be drawing circles and speaking to agents.
Yeah, SEO is, I'm really fascinated to see how quickly that world changes.
And it could still be slow, I don't know, right?
But that fascinates me.
Yeah, just on the idea of it being one of the first from an AI on device manufacturers,
the Google Pixel 8 and the Pixel 8 Pro, they have a lot of AI baked into them as well.
I don't think as much as this particular device.
OnePlus, which is the manufacturer of device that I use, they just announced their OnePlus
12 device this week and Qualcomm were there at the keynote talking about the on device
AI capabilities of that phone.
Again, don't think it quite matches that of the Galaxy S24 series, but we're seeing more
and more of this coming on device.
So we're probably going to see most flagship devices launched over the next year being
capable of running, I think, 10 billion parameter models on device.
Yeah, that's interesting.
And ultimately, we don't want to give them wrong impression here on the podcast.
AI has been underpinning a number of things in phones for years, right?
If you use Google Photos and it's got smart tagging capabilities and you can type in the
word dog into your search bar and it will go find all your pictures with dogs in.
So it's less that phones have got AI now, I think phones always had AI or have had forms
of AI for many years.
I think what's interesting here is models on device and everybody getting on the AI hype
train and making AI a differentiator for their platform, which will very soon not be a differentiator
probably.
I think one of the things I thought was cool here, and I don't know how it will play out,
is some of these onboard, assuming they're onboard, transcription and translation services.
Because one of my use cases back in the day for Otter, which is a transcription, one of
the first transcription services, was pre pandemic having my phone, putting it on the
table for a meeting and then having Otter transcribe it for me.
But if a lot of that stuff ends up just onboard my phone as it is, am I going to stop using
certain platforms like Otter for creating transcriptions of calls, for example.
And I think that transcription eMarkets in extreme dynamic flux, right, because it's
being baked into Teams and Zoom and Google.
I think if I was CEO of Otter and similar tools, I'd be a bit worried honestly, because
it's just being baked into everything.
Yeah.
So I use a tool called Laxis.
We've had Eric from Laxis on the podcast recently.
I use this in Google Meet all the time and Google Meet has recently launched its own
transcription tool that you can have turned on.
And if you join a meeting where it's turned on, it tells everybody that it's being transcribed.
I still use Laxis over, even though I'm in the Google workspace ecosystem, I still use
Laxis.
And the reason for that is Laxis can join calls in other services.
So by having it connected to my calendar, if it's my Google Meet, it just transcribes
it in the background.
But if I get invited to a Teams call for a client or something like that, I still get
my own transcription because Laxis joins that call.
So the idea of it being cross-platform is really critical for me.
Interesting.
Yeah, I think that makes a lot of sense.
Right, two more stories listeners and then we'll let you to go about your day.
The first one very quickly is an interesting story where AI was being used for political
misinformation.
So you've probably seen this in the news.
It's been fairly high profile, but basically a robo-caller using a fake AI generated voice
of President Joe Biden urging Democrats to stay home and not participate in primary elections.
And we talked about this a bit on the podcast and we talked about 11 Labs today.
The ability of AI to clone and mimic people's voices and with tools like HeyGen, even video,
we're drifting ever closer to that world where you can't trust what you see.
You can't trust a mid-journey image.
You can't trust a voice.
You can't trust a video.
I know people who are already talking about having like safe words or stories that they
can share with each other to prove that it's actually them that's calling, right?
Like your grandmother rings you to say that she's got herself in some trouble and she
needs you to send some money and it sounds exactly like her, but actually it's synthetic
voice for example.
And this is the first sort of mainstream media example of it being used to influence pretty
major processes like the US election cycle.
So we won't go into too much detail on this, but I think as marketers and communications
professionals we will need to work hard to set the standard to build trust in content
that's supported with AI tools because the amount of mistrust that's going to get generated
by bad actors is going to be large enough.
So labeling things as being generated by AI or supported with AI I think is going to become
more and more important as the tools' abilities to pull the wool over our eyes only increase.
And so it's probably something that we all as humans need to be aware of in terms of
fact checking and really being sure that we can trust our sources, but as marketers maybe
a bit of extra requirements upon us to do the right thing in these areas.
As we see more and more examples of it, and today there was the example of Taylor Swift
with some deep fake pornography shared widely on Twitter and she's come out and said this
is terrible and it's got lots of headlines.
As we see more and more examples of this, I think people are going to get more and more
aware of it.
When it's high profile cases like Joe Biden, like Taylor Swift who let's be honest is the
biggest celebrity in music right now, people are going to get smarter and more discerning.
I like to think, I have trust in humanity where people will just take a second and say,
hold on, is this real?
I think you need to be worried Martin because for me you're the biggest celebrity in the
world of AI and marketing and so having someone clone your voice from this raw source podcast
here I think is a major risk for you.
Yeah and there's enough video material out there that they might turn it into some deep
fake porn as well so strap in everyone.
I'm glad you didn't mispronounce your words there but yes, usually most podcast hosts
would edit this bit out but we're not going to do that.
Let's move on to our last story Martin.
Let's go with this Microsoft Copilot.
We talked a bit about ChatGPT for Teams and how finally small businesses can tap into
the commercial power of ChatGPT without having to worry about the information they put into
ChatGPT.
Hot off the heels, pretty much within a day or two.
Microsoft Copilot.
What happened here?
They have released Microsoft Copilot to everybody so it was previously restricted to organizations
that had, was it 300 seats I think you needed to buy.
Whereas now they're saying organizations of any size can sign up maybe a minimum of two
seats for the business version, I'm not entirely sure.
But even the home and family edition of Microsoft 365 has a Copilot upgrade as well which means
that you now have access to Copilot Chat which was previously Bing Chat.
You have Copilot in Outlook which does summarization of emails as well as drafting replies for
you.
Copilot for PowerPoint which is giving you a starting point if you just write a bit of
a prompt it will create the PowerPoint deck for you.
And some really cool demos of that actually and it does a pretty good job, a kind of B
plus level performance.
I'm not going to knock it like I did when we reviewed the Google Duets, Google Slides
version.
It seems to do a good job when you provide it with like a Word document.
So like say you've got a report that you've written and you need to turn it into a presentation
it seems to do quite a good job of doing that conversion.
So I think that's going to save quite a lot of people time actually to get that starter
for 10 in terms of getting it all the material put into a PowerPoint style laid out and all
that stuff.
They've also got Copilot in Word which is pretty much bringing content generation into
Microsoft Word and Copilot in Excel which at the moment is quite limited and not really
worth talking about.
It's the area that I have the most hope for because I think when that gets good we'll
start to see some very exciting data analysis functionalities put into the hands of everybody.
Pricing of this is $20 per month per user and is available now.
Yeah, it's a fascinating one.
I reflect back on us getting super excited about the Microsoft Copilot like launch video
which was about last March, April time.
So it's taken the best part of a year for this to be in the hands of everyone which
I think relatively speaking for what they've had to build and deploy and test it through
the larger organizations, the enterprises that have had access is probably fair game
but it's not quite as powerful as the demo made it look and we still don't have the
Excel part.
So it comes back to an earlier part of our conversation, Martin, in terms of if you see
something cool in a research paper or a demo, how long until the power of that is in your
hands?
Probably 6 to 12 months, probably closer to 12 and reduce your expectations on that tool
by 25 to 50% based on what you saw in the demo.
Seems to be a rule of thumb in terms of the commercial application of a lot of these AI
based tools, especially the generative ones.
Right, I think we'll call it there.
Thank you for sticking with us folks.
There was a lot to cover this week.
Thank you to you, Martin, as always for your time and insights.
Much appreciated.
Well, thank you and I hope you can just have a moment to sit and think about what Jürgen
Klopp has done for Liverpool and be thankful that it happened.
You know, I managed to forget that over the course of the last hour and now you've just
brought it all back for me.
Sorry about that.
It's always 5pm somewhere so it's probably time for a beer and a tear.
But yes, that's now going to take up the focus for me for the rest of the day.
I'm sure I've got nothing better to do.
Yes I do.
Right, listeners, thank you for your time.
We'll look forward to giving you the news in our next episode.
Until then, bye everyone.